International Journal of Mechanical Engineering and Robotics Research
IJMERR is a scholarly peer-reviewed international scientific journal published bimonthly, focusing on theories, systems, methods, algorithms and applications in mechanical engineering and robotics.
Assessment of Dynamic Loads on a Railway Track from Open Wagons in a Worn Condition with an Increase in Axial Load
The purpose of the presented article is to evaluate the dynamic loads affecting the railway track during the operation of gondola cars in a worn-out technical condition under conditions of increased axial load. The analysis of the features of the interaction of a gondola car with the upper bed of the railway track, as well as the effect of operational wear of components and elements of trolleys on the nature of the transmitted loads, is carried out. An experimental study and simulation of the dynamics of a gondola car has been performed, taking into account the technical condition indicators, loaded and empty condition of gondolas. The results obtained during the study make it possible to identify critical parameters affecting traffic safety and wear of the railway track, as well as formulate recommendations on acceptable indicators of the coefficient of stability margin and the condition of operation of worn-out rolling stock in the study of horizontal and vertical loads.
Topic: Fracture Mechanics and Fatigue Analysis
Copyright (c) 2025 International Journal of Mechanical Engineering and Robotics Research
Fault Diagnosis in Vehicle Air Conditioning Systems via Comparison of k-Nearest Neighbors and Random Forest Classification Models
This study compares the performance of k-Nearest Neighbors (kNN) and Random Forest (RF) models for fault diagnosis in vehicle air conditioning systems. A dataset of 745 validation samples and 568 test samples was used, covering seven fault conditions, including normal operation and varying levels of condenser fouling and refrigerant leakage. The models were evaluated using accuracy, precision, recall, and F1 score to measure their fault classification effectiveness. Both models achieved a validation accuracy of 91.68%, with a slight drop to 89.26% during testing, indicating similar generalization abilities. Precision remained consistent across both models, while recall and F1 scores varied. The kNN model showed higher recall, indicating better detection of true positive fault cases, while the RF model exhibited more balanced performance with stable precision and F1 scores across datasets. ROC curve analysis revealed that RF outperformed kNN, especially in distinguishing multiple fault classes with overlapping features. However, both models struggled with intermediate levels of condenser fouling, as highlighted by confusion matrix analysis, suggesting a need for improved feature differentiation. Overall, the RF model demonstrated greater stability and robustness, making it more suitable for diagnosing vehicle air conditioning faults where consistency is critical.
Design and Performance Assessment of a Mini Underwater Robot
This study focuses on the design and experimental evaluation of a mini underwater robot specifically for detecting leaks in tanks or reactors. This mini-robot are designed to overcome the challenges of inspecting and maintaining reactors, especially in areas that are difficult to access. A primary concern for effective navigation is motion stability; unstable movement can compromise data accuracy and leak detection precision. The robot operates as a remotely operated vehicle (ROV), controlled from above the water via a wired connection that supplies both power and telemetry. It is equipped with four 60 mm diameter propellers, each featuring three CW/CCW blades, and a front-mounted camera. Constructed from Polylactic Acid (PLA), the robot's dimensions are 390 x 85 x 85 mm, and it is powered by a LiPo battery. The selection of PLA offers a balance of lightweight construction and sufficient structural integrity for underwater maneuvers. Testing occurred in a site measuring 5700 x 2080 x 1100 mm, providing a controlled environment to assess the robot's performance. Experiments involved guiding the robot along various paths to assess its reliability. The results demonstrate effective control with minimal error rates on certain paths, highlighting the importance of stable movement and the potential of these mini underwater robots for intricate underwater operations. This demonstrates the potential for deploying such robots in complex industrial environments where precision and stability are paramount.
Robust Control of a 2D Rehabilitation Robot Using Admittance and RBF Neural Network
In the dynamic model of the 2D rehabilitation robot, factors such as modeling errors, uncertain external disturbances, and friction are present. To achieve compliant force interaction control, a hybrid control strategy integrating admittance control, adaptive RBF neural network compensation, and sliding mode control is proposed in this paper.First, the desired velocity is generated based on the interaction force at the robot handle through admittance control, allowing compliance to be maintained during the interaction process, thereby enhancing the comfort and safety of rehabilitation training.Second, the unknown nonlinear dynamics of the system are approximated in real time using an RBF neural network, and an adaptive learning rate mechanism is designed to accelerate the network's convergence and improve the compensation accuracy.Finally, sliding mode control is employed to compensate for neural network approximation errors and external disturbances, thereby enhancing the robustness and stability of the closed-loop system.Through simulation experiments, the proposed method has been demonstrated to outperform the pure admittance control strategy in terms of trajectory tracking accuracy, response speed, and disturbance rejection capability, verifying its effectiveness and superiority.
Admittance Control; RBF Neural Network; Sliding Mode Control; Adaptive Control; Rehabilitation Robot
Crack Interactions of Non-Coplanar Internal and External Surface Cracks of Hollow Thin Cylinders
The interaction of noncoplanar internal and external surface cracks in hollow thin cylinders plays a critical role in structural integrity assessments, particularly under cyclic and multi-axial loading conditions. This study investigates the behavior of noncoplanar parallel cracks under tension, bending, torsion, and mixed-mode loading, focusing on the distribution of normalized stress intensity factors (SIFs) and the interaction effects along the crack front. Results indicate that external cracks exhibit asymmetric SIF distributions with both amplification and shielding effects, while internal cracks primarily experience shielding. Crack depth (a/t ratio) and separation distance (s/L) significantly influence the interaction magnitude, with deeper cracks showing stronger amplification effects. Under torsional loading, shielding dominates, and interaction effects diminish with increased crack separation. Additionally, the inclination angle affects crack interaction behavior, with external cracks demonstrating greater amplification, making them more critical for structural failure. The findings provide valuable insights into crack growth behavior and fracture-mechanics in thin-walled cylinders, aiding in the development of improved damage tolerance design and life extension strategies for critical structural components.
crack interaction, non-coplanar surface cracks, hollow thin cylinder, stress intensity factor, ANSYS finite element analysis, fracture mechanics, crack assessment
Energy-Saving Research on Battery-Powered Load-Haul-Dump Machine Based on Dual Motor Drive and Intelligent Gear-shifting Control
With the increasing environmental requirements in the mining industry, battery-powered underground Load-Haul-Dump (LHD) machines have become mainstream mining equipment due to their zero emissions and low noise. However, the limited battery capacity poses a critical challenge in improving energy utilization efficiency and extending the working time per charge. This study proposes an energy-saving strategy based on dual motor drive and intelligent gear-shifting control to optimize energy efficiency. Firstly, a model of the LHD’s drive system and energy consumption is established to analyze energy usage characteristics under different working conditions. Then, an adaptive genetic algorithm is employed to optimize transmission system parameters. Furthermore, an intelligent energy-saving gear-shifting strategy based on a Backpropagation Neural Network (BPNN) is proposed to achieve precise gear control and reduce energy consumption. Simulation results indicate that the proposed strategy reduces energy consumption per working cycle by 12.7%, significantly enhancing the economic efficiency and operational performance of the LHD. This research provides a theoretical basis and engineering reference for energy-saving control of underground LHDs.
Cutting Force and Cutting Temperature Prediction in Drilling Using Finite Element Analysis
Drilling is a crucial metal cutting operation extensively employed in the mechanical engineering sector. Predicting the temperature and cutting forces (forces and torques) occurring during drilling on any material and affecting the drill bit will play a positive role in estimating the life of the drill bit and thus achieving better hole quality and accuracy. In this study, drilling force and temperature were studied during Aluminum drilling using finite element method as an alternative to experimental. This topic aims to determine the cutting force and temperature in the cutting zone by analyzing the input parameters of cutting speed and feed rate during the simulation of a high-speed steel drill bit on AL 7075-T6 material. This paper introduces a methodology for developing two finite element models: the finite element method (FEM) and smoothed particle hydrodynamics (SPH) for drilling simulation. The simulations are performed using the LSDYNA code based on available real experiments. The accuracy of the model makes it a satisfactory approach for drilling experiments. The two presented models can verify the drilling process and predict the heat and cutting forces occurring during drilling operation.
Solution of the Positioning Problem for a Planar Parallel Manipulator Based on a Third-Class Mechanism
This paper presents a kinematic analysis of a three-link manipulator system, focusing on the calculation and visualization of joint angles (q1, q2, q3) as functions of the end-effector coordinates (xS, yS) and the orientation angle (α). The goal is to determine the values of the generalized coordinates (q1, q2, q3) that allow the manipulator's working body (WB) to reach a given spatial point S in its workspace. The system's configuration and inverse kinematics problem are addressed through geometric and trigonometric constraints, with the joint angles determined by solving relevant equations involving the manipulator’s link lengths and the positioning of the end-effector. We introduce a redundancy elimination technique by minimizing the positioning error, ensuring the accuracy of the manipulator's trajectory. The results are visualized using 3D plots, which depict the relationship between the joint angles and the coordinates of the manipulator, aiding in the understanding of the system's behavior under different configurations. The proposed method demonstrates efficient handling of kinematic redundancy and offers insights into optimizing the manipulator's positioning accuracy. The findings contribute to improving the control and motion planning of robotic systems in precision tasks.
Advanced Diagnosis of Bearing Defects: A Multi-Domain Approach
It is possible to identify faulty rolling-element bearings before they fail. Regularly measuring the overall vibration level at the bearing housing is the simplest method for detecting such issues. However, routinely evaluating the vibration spectrum provides the earliest potential warning. Rolling element bearings, which combine the functions of both rollers and balls, are commonly found in many applications. Rotary shafts are used in a wide range of devices, including gas turbines, power generators, water pumps, motorcycles, conveyors, and various industries. This work introduces a new approach for detecting, analyzing, and diagnosing rolling element bearing defects. The study compares these defects across three important parameters: the time domain, frequency domain, and spectrum plot. To enhance the effectiveness of spectrum analysis, the study considers the spectrum generated from faults in the outer race and inner race, along with normal conditions.
Diagnosis of Rolling Element Bearing Defects; Time Domain; Frequency Domain; and Spectrum Plot Approaches
The Procedure for Establishing the Permissible Speeds of Semi-Wagons
The article presents an analysis of the permissible speeds of rolling stock during straight-line movement and when passing curves of different radii. The dynamics, structural strength of metal constructions, track impact, parameters of unbalanced acceleration in curves, and stability against wheel derailment are considered. The methodology is based on calculations of dynamic characteristics and modeling the interaction between rolling stock and track infrastructure. The results can be applied in the design of railway transport, modernization of rolling stock, and the development of regulatory documentation to ensure traffic safety
Safe Reinforcement Learning Using Sequential Constraints for Collision Avoidance
A high level of safety has been considered as a key issue in collision avoidance algorithms because collisions are fatal for robots. In this paper, we present a novel safe reinforcement learning (SafeRL) approach for robots to learn a collision avoidance technique using sequential constraints. First, we classify the risk of collision into the following three danger levels: safe, risky, and critical levels. In the safe level, the output control of RL is directly applied to the robot’s navigation because of its low risk. In the risky level, a control barrier function (CBF) is used to ensure the safety of the robot while achieving its control objectives. While the CBF is primarily used to prevent the robot from entering unsafe areas, but it does not provide the ability to recover once it has entered a dangerous area. Therefore, a recovery function (RF) should be used to avoid obstacles at the critical level when the obstacle is too close to the robot. The RF helps the robot to regain stability or return to a safe state after a disturbance or constraint violation. Finally, we propose a control recovery and barrier function (CRBF) based on CBF and RF, which calculates constraints at each level of danger and performs different controls depending on the risk of collision. We conducted in both simulated and real environments to compare the results with existing collision avoidance algorithms. Our method achieved a collision avoidance success rate 5.2%-22.5% higher than APF and ORCA algorithms in environments where obstacles are faster than the robot.
Hybrid Modelling, Control and Simulation of Knee Joint Actuated by Antagonistic Pneumatic Artificial Muscles
This paper presents a hybrid modeling and control strategy for a knee joint actuated by antagonistic McKibben pneumatic artificial muscles. The approach integrates a static force model, which captures the relationship between muscle contraction ratio and internal pressure, and a dynamic model that accounts for friction effects. A compressible flow model describes how pressure is established within each muscle during inflation and released during deflation. By unifying these elements with a differential equation for the knee joint’s motion, a comprehensive representation of the system is developed. A Proportional–Integral–Derivative (PID) controller manages the discrepancy between the reference trajectory and the actual knee angle, adjusting the valve orifice area to regulate airflow accordingly. Simulation studies performed in Simulink confirm the validity of the static muscle model against published data, while the dynamic and flow models are benchmarked against relevant experimental results. The system’s closed-loop performance is evaluated using multiple trajectories—including square wave, triangular, sine wave, and a human gait cycle—quantified through root mean square error (RMSE), maximum absolute error (MAE), and normalized RMSE (NRMSE). Findings indicate that this methodology provides a robust basis for future advancements in pneumatic actuation for robotics applications.
McKibben pneumatic artificial muscles, Knee joint, antagonistic actuation.
The Effect of Pitch Angle Amplitudes on a Cyclorotor Performance Using ComputationalFluid Dynamics
Currently, multirotor UAVs commonly use propellers rotating the vertical axis. However, the cyclorotor is a type of multirotor that the propellers rotate around the lateral axis. The advantages of this type are more compact, stiffer, and safer than conventional rotors. This article focuses on studying the aerodynamic behavior of cyclorotors. The objective of this article is to study the effect of pitch angle amplitudes on the lift, propulsive force, torque, and lift-to-drag ratio using 2D Computational fluid dynamics (CFD) simulation. The variables of this study were as follows: the pitch angle amplitudes of 25, 35, 45 degrees and the rotor speeds consisted of 400, 500, 600, 700, and 800 RPM. As a result, (i) The pitch angle amplitude of 45 degrees provided the highest lift of 29.717 N at 800 RPM. (ii) The pitch angle amplitude of 45 degrees produced the maximum propulsive force of 11.026 N at 800 RPM. (iii) The pitch angle amplitude of 25 degrees offered the lowest torque of 0.239 N.m at 400 RPM. (iv) The pitch angle amplitude of 25 degrees promoted the highest lift-to-torque ratio of 12.705 at 800 RPM.
Van and Truck Classifications of 3D LiDAR Perception for Autonomous Vehicle Navigation
Autonomous vehicles, known as self-driving cars, rely heavily on the 3D LiDAR sensor alongside other sensors to navigate autonomously. 3D LiDAR sensor produces sparse point clouds which makes classifying and detecting objects inside them difficult. However, the introduction of deep learning made it possible which led to various types of 3D object detectors for 3D point clouds. Existing 3D object detectors can identify cars, pedestrians, and cyclists. However, on the road with the abundant presence of heavy vehicles like trucks and vans, this paper explores the potential to retrain an existing 3D object detector, PointPillars, using transfer learning, to also detect vans and trucks in the 3D LiDAR. By training it to do so, autonomous vehicles can recognize and respond appropriately to heavy vehicles during navigation. Then by using Average Precision metric, the results showed an average performance for ‘Van’ but a below-average performance for ‘Truck’. Interestingly, there’s an improvement on the Average Precision score for the ‘Car’ and ‘Cyclist’ classes with a drop on the ‘Pedestrian’ class. The modified PointPillars achieved mean Average Precision (mAP) scores of 35.53 and 34.57 for the 'Van' class using Bird's Eye View (BEV) and 3D metrics, respectively, at IoU=0.5 and at ‘Moderate’ difficulty. On the other hand, the mAP scores for the 'Truck' class are 10.09 and 4.46 using BEV and 3D metrics, respectively at IoU=0.5 and at ‘Moderate’ difficulty.
3D Machine Learning, LiDAR, transfer learning, autonomous vehicles (AV)
Integrating Vision-Language Models for Enhanced Robotic Grasping and Interaction Using RGB Image and Prompt
Object detection and grasping is one of the critical challenges in robotic research, particularly when working in complex environments with diverse objects in terms of shape and position. Although methods using RGB images have shown promising results in simpler scenarios, they still face numerous issues in more complex scenes, especially when objects overlap. Furthermore, prior research has primarily focused on object grasping, without focusing on addressing the interaction capabilities between robots and users during the grasping process. Recent advancements in vision-language models have opened up significant potential for the development of human-robot interaction systems based on multimodal data. This paper presents an integrated model combining computer vision and language models to enhance object detection and grasping capabilities in real-world environments. The proposed approach consists of three key steps (1) identifying the locations of objects and generating segmentation masks using a visual-language model; (2) grasp candidates are predicted from the generated masks and bounding boxes via the Grasp Detection Head; and (3) the candidates are optimized and refined using the Grasp Refinement Head. The integration of vision-language models in the proposed approach not only enhances the ability of robot to understand the semantics of language, enabling more accurate grasping decisions, but also strengthens the interaction capabilities of robot with users. Experimental results demonstrate that the proposed model achieves higher grasping accuracy compared to existing methods, particularly in complex scenes with multiple objects. Proposed model achieved 93.12% accuracy on Jacquard dataset. Additionally, the model also demonstrates its ability to understand complex contexts through Interactive Grasp experiments.
Assist-as-Needed Gait Rehabilitation via Actuator Torque Estimation for a Self-Balancing Lower-Body Exoskeleton
Individuals with partial lower-limb impairments can benefit from robotic gait rehabilitation to enhance muscle memory and coordination. Lower-limb exoskeletons can provide assistance tailed to a patient's needs, reducing therapist workload by automating the procedure. However, the level of assistance provided by the robot should be controlled, such that the user receives assist as needed, while remaining engaged in the motion. The main focus of this paper is to introduce a human-robot interaction estimation technique that enables a self-balancing exoskeleton to asses patient-initiated joint torques; using only robot actuator torque estimations. An admittance controller is used to adjust the deviation from the target based on the effort of the user. The proposed method is tested in simulation environment, to control the swing foot of an exoskeleton biped robot. The simulations show promising results, indicating the potential for applying the method in experiment and more complex motions.
Dual-adaptive suspension system for fighter jet air-inlet inspection robots
This paper presents the comparative analysis of the dual adaptability mechanical suspension system for a mobile robot inspector of the air intakes of a Dassault Mirage 2000P fighter jet with independent suspension coil springs and solid axes, which are two of the most widely used suspension systems for wheeled mobile robots for confined space inspection. This mechanism allows the variation of the relative position of each wheel thanks to two lateral joints that link the two pairs of longitudinal wheels to a central joint that controls the camber. The experimental analysis compared the mechanical advantages of adaptability, maneuverability and locomotion efficacy with two of the most used suspension types in mobile wheeled robots for inspecting pipelines, ventilation ducts, tunnels and collapsed buildings due to the partial geometrical similarity of their working environments with the inspection route of air intakes in airplanes. Experimental locomotion tests are carried out on flat and cylindrical surfaces generated from the geometric decomposition of the inspection area of the fighter plane to finally perform them on the air intakes of the aircraft, demonstrating a significant traction and maneuverability advantage over conventional systems on compound surfaces based on concave and convex curves.
Formation Control of Mobile Robots Using a Beacon-Based Algorithm with Alternating Node Activation
In this study, we present a dynamic robotic formation control algorithm designed to manage multiple nodes of robots that alternate activity in a coordinated manner. The approach leverages a combination of alternating activation, formation maintenance, and collision detection mechanisms to ensure robust performance in environments with multiple robotic units. Specifically, the algorithm activates different nodes of robots in a predefined sequence, allowing them to take turns executing tasks while maintaining a stable formation. Each robot in the active node computes its direction based on a potential field method, balancing attractive and repulsive forces to maintain desired distances from other robots and stationary points. Additionally, our algorithm integrates collision detection to prevent overlap and ensure safe movement of the robots. This framework was implemented and tested in a simulated environment, demonstrating its effectiveness in maintaining formations and preventing collisions. Our results highlight the potential of the proposed method for applications in autonomous multi-robot systems, such as search and rescue, environmental monitoring, and warehouse automation.
Topic: Recent Advances in Mobile Robotics Navigation
Dynamic robot formation, multi-robot system, collision detection, formation algorithm, autonomous robots, alternating activation, simulation
Optimal Multi-stepped Sonotrode Design for Ultrasonic Plastic Welding
In this study, three-dimensional FE modal analyses were conducted to examine the Sonotrode's vibration characteristics. Since the excitation frequency was 20 kHz, the frequency range was set between 15 and 35 kHz. The density and elastic modulus of the Sonotrode material, AL7075, were 2802 kg/m3 and 71.9 GPa, respectively. FE modal analyses were conducted using ANSYSTM Because of the AL7075's good mechanical properties and excellent ultrasonic transmission capabilities Represented by high amplitude, it was utilized. A quadratic mathematical model of the displacement amplitude based on the dimensions was developed using the response surface methodology. When compared to the RSM results, the study's conclusions demonstrated that the ideal Sonotrode, which yields the highest value for the performance input, could be found using the recommended RSM-GA integration technique. The ideal entrance dimensions are then ascertained by combining the RSM model with GA. The ideal results showed that the highest vibration amplitude is 57 microns, and it was also found that the variables X2 and X4 do not affect the vibration amplitude.
Optimal design, ultrasonic welding, stepped sonotrode, plastic welding, harmonic response, response surface
Research, Design, and Development of a Sand Drawing Robot
This paper presents the research, design, and fabrication of a sand drawing robot capable of rendering detailed images on sandy surfaces. The robot integrates a robust mechanical system optimized for navigation and stability on deformable terrain, addressing challenges unique to sandy environments. Advanced control algorithms are developed for precise movement and drawing execution, ensuring consistent line quality and pattern accuracy. A user-friendly interface allows for custom image input, which is processed through sophisticated path-planning algorithms to generate executable instructions for the robot. Experimental results demonstrate the robot's ability to accurately reproduce complex designs, highlighting its potential applications in interactive art installations and educational settings. This work contributes to the field of robotics by enhancing the precision and reliability of autonomous robots operating in dynamic and unstructured environments.
Development and Implementation of Autonomous Mobile Robots for Warehouse Applications
This paper presents the development of an autonomous mobile robot specifically designed for indoor warehouse applications, utilizing advanced Lidar laser sensor technology to detect and avoid obstacles efficiently. By implementing sophisticated navigation algorithms, the robot can autonomously traverse the warehouse environment, minimizing the need for human intervention. A key focus of this research is the creation of a mobile robot model that is not only highly functional but also cost-effective, making it accessible for organizations with budgetary constraints. The robot's capability to localize itself within the indoor environment and generate optimal paths to specified locations enhances its operational efficiency. This blend of cutting-edge sensor technology, intelligent algorithms, and economical design marks a significant step forward in the field of autonomous mobile robotics, particularly in Vietnam’s warehouse automation scenarios.
Emergency UAV Delivery Framework: A Hybrid Approach to GPS Navigation and Visual Landmark Detection
This study develops an innovative Unmanned Aerial Vehicles (UAV) framework incorporating GPS and Landmark Detection to optimize relief delivery processes in emergency scenarios, particularly in expansive regions with intricate conditions. The system leverages extensive positioning capability of GPS to navigate UAVs toward target zones, subsequently employing Landmark Detection to ascertain precise drop-off locations. Notably, an Archimedean spiral trajectory algorithm is deployed under unstable GPS conditions, enabling UAVs to expand search coverage and enhance landmark detection capabilities, even in obscured areas. Experimental results reveal a precision drop rate of 98% at an altitude of 5 meters, ensuring accurate delivery. Additionally, the landmark detection success rate under unstable GPS conditions achieved 100% when the overlap ratio of camera frames reached half the width of frame (1/2W), demonstrating high efficacy in mitigating target omission risks. This approach not only minimizes delivery time but also enhances operational flexibility, facilitating rapid and precise UAVs access to critical relief zones. The proposed system exhibits significant potential to deliver effective solutions for emergency relief missions, meeting stringent demands for speed, accuracy, and stability in time-sensitive delivery operations.
Modeling of Cyborg-Cockroach Swarm Using Agent-Based Simulation
This study investigates the behavior and performance of Cyborg Insect Swarms operating under a decentralized control mechanism. Cyborg insects, which integrate living insects with miniature electronic controllers, represent an innovative approach in swarm robotics, combining biological capabilities with artificial control systems. We utilized the Multi-Agent Robot Swarm Simulation (MARSS) platform to model and analyze the dynamics of these hybrid entities in various simulated environments and scenarios. The research aimed to understand how cyborg insect swarms perform compared to traditional robotic swarms, particularly in terms of adaptability, efficiency, and coordination in complex tasks. We conducted extensive simulations replicating diverse environmental conditions and task requirements to assess swarm behavior, inter-agent communication, and overall swarm efficiency. The study focused on key performance metrics including task completion time, energy efficiency, and adaptability to unexpected obstacles or changing objectives. Results demonstrated that cyborg insect swarms exhibited enhanced flexibility and adaptability in complex and dynamic environments, effectively leveraging the natural sensory capabilities and agility of insects. The bio-hybrid nature of the swarm allowed for more nuanced responses to environmental stimuli compared to fully artificial systems. However, the research also revealed challenges unique to cyborg swarms, particularly in maintaining consistent swarm behavior due to the inherent biological variability among individual insects. Despite these challenges, the study highlighted potential advantages of cyborg insect swarms in applications such as environmental monitoring, disaster response, and search-and-rescue operations. These findings contribute significantly to the emerging field of bio-hybrid systems and provide valuable insights into the practical implementation and potential real-world applications of cyborg insect swarms.
Design Considerations for the Design of Friction Pickers in Use for Runway Cleaning: An Experimental Study
The present work studies the behavior of friction skimmers as a method for extracting foreign object debris (FOD) from runways by experimentally analyzing the components of this skimmer, at the same time providing a vision of the influential variables in the design of these skimmers. With this objective, a test scenario based on the Federal Aviation Administration (FAA) circulars is proposed, conformed by a variable speed strip that simulates a category 5 concrete runway and blades that simulate the dustpan, which are used in different test methodologies that allow calculating the most efficient values among the different study variables, from which a friction dustpan with more than 90% efficiency was obtained when it operated at more than 12.24km/h.
Topic: Autonomous Robots in Industry 4.0
Design and Simulation of a Foot Prosthetic Testing Machine Compliant with ISO 10328 Standard
This study addresses the critical need for evaluating the mechanical performance of prosthetic feet in Indonesia, where a lack of suitable testing machine impedes the production of high-quality prosthetics that meet national and international standards. Following the ISO10328 Structural Testing of Lower Limb Prostheses, we aimed to design and enhance a testing machine for prosthetic feet using SolidWorks and Ansys. The machine, constructed from AISI 1020, SS 201, and AA 6061 materials, underwent finite element analysis to assess deformation, stress, and safety factors under a maximum load of 322 kg, incrementally tested at 161 N/s. Results revealed deformation values of 0.16751 mm, 0.16043 mm, and 0.43303 mm, along with safety factors of 2.1439, 2.0921, and 1.8741, respectively. Material selection considerations and von-Mises stress simulations led to the choice of AISI 1020. To address localized stress, design optimization introduced a collar, enhancing the safety factor from 2.1439 to 3.225. This comprehensive approach to testing equipment design and optimization is vital for ensuring the reliability and conformity of domestically produced prosthetic feet in the Indonesian market.
Neural-Backstepping Adaptive Control for Nonlinear Motion of Sliding Mobile Robots
The paper deals with the design of a controller using neural networks to track the motion trajectory for a differential mobile robot considering the lateral and longitudinal slip components of the wheels. The proposed control algorithm is an adaptive controller using neural networks to online adjust the parameters of the Backstepping controller according to the robot's reference model and trajectory. In this method, the neural network is required to find the characteristics of the kinematic and dynamic model and use it to determine the parameters for the Backstepping controller, thereby increasing the trajectory tracking performance and reducing error and index to compensate for the gains of the backstepping controller. The Lyapunov criteria is used to assess the closed system's stability. The WMR algorithm is built on a very flexible microcontroller architecture, and the software supports ROS robot programming. Simulation results with Matlab/Simulink reveal that the effectiveness of the proposed controller exhibits a steady state and position deviation of the right and left wheels is accordingly 0.0021 (m) and respectively 0.003(m) and the angular velocity tracking error in the right and left wheels of the control method is 0.006 (rad/s). Experimental results have verified outstanding efficiency with the most significant control error of only about 0.0053(m) in the environment around the room with many curves and 0.0021(m) in the corridor environment when testing with PID-Backstepping NN controller compared with PID controller, PID-Backstepping controller
Force/ Position Control Based on PSO and Approximately Jacobian for 3-DOF Parallel Robot for Bone Drilling
The aim of this study is to propose a hybrid position/force controller for a 3- degrees of freedom (3-DOF) parallel robot is used in bone drilling in orthopedic trauma surgery. In this paper the authors propose a position/force hybrid control method for a 3- DOF parallel robot. The 3-DOF parallel robot consists of three arms and three parallelograms mechanism with rotational and spherical joints. Firstly, a mechanical structure with 3 single-axis sensors for force sensing is proposed. Second, the dynamic equation is built by using the first type Lagrangian equation. Third, a position/force hybrid controller with a synchronization calibration matrix was designed. In this research, the force controller is based on the Jacobian approximation matrix of the parallel robot. Simulation results on Matlab software show that using the approximate Jacobian matrix reduces the calculation time by half. Lastly, some experimental results show the effectiveness and accuracy of the proposed controller.
Parallel robot, Trajectory tracking control, Position/Force control, Trajectory tracking, Jacobian approximation matrix, POS
Nonlinear Vibration Analysis of Metal Foam Nanobeams with Varying Porosity
This paper investigates the geometrically nonlinear vibration response of thinned thick porous nanobeams. To achieve this, we employ nonlocal elasticity theory to formulate the behavior of the nanobeam. The presence of voids or pores significantly influences the material properties of the nanobeam; thus, this study takes into account their effects, including various void distributions. We utilize the closed-form solution for the nonlinear problem established in a previous paper. Subsequently, we examine the impact of nonlocal fields, void distribution, the number of voids, and geometrical properties on the nonlinear vibration characteristics of the nanobeam.
Topic: Recent Innovations in Mechanical Vibrations
Characteristics of Side Force and Fluid Flow Separation Past Vertical Stabilizer and Rudder of Microlight Aircraft Model
This study aims to identify the characteristics of side forces, drag forces, flow separation, and pressure contours on the vertical stabilizer and rudder models of the PPH-Unhas microlight aircraft. Two models were made according to the PPH-Unhas prototype and tested using experimental methods conducted in a low-speed wind tunnel and computational methods using FLUENT 6.3.26 software. Tests were conducted at three airflow velocities, namely 10 m/s, 16 m/s, and 22 m/s. Each speed level was treated with 10 (ten) changes in aircraft rudder deflection angle (δ) from 0˚ to 45˚. The results showed that the highest side force (FS) and drag force (FD) were achieved at 2.008 N and 1.010 N, respectively, at an angle of δ = 45˚ with V = 22 m/s. The maximum side force coefficient (CS) of 1,903 was obtained at δ = 25˚ with V = 22 m/s, while the highest drag coefficient (CD) of 1,090 occurred at δ = 45˚ with V = 10 m/s. At V = 22 m/s, flow separation at an angle of δ = 5˚ occurs early at tapping point 7 with pressure coefficient (CP) = -0.028, at δ = 25˚ at tapping point 9 with CP = -0.056, and at δ = 45˚ at tapping point 10 with CP = -0.444. The best pressure contour is obtained at δ = 25˚, so the optimal angle for the rudder is δ = 25˚
Feedback Control Using ARMA to Maneuver Wheelchairs
Electric wheelchairs are increasingly utilized by individuals with lower motor disabilities for daily mobility. These users typically operate their wheelchairs using a joystick controller, yet those with cerebral palsy encounter challenges in effectively maneuvering the joystick. This study presents a novel approach called Feedback Control using ARMA to Control Wheelchairs (FCWCARMA), which is evaluated against a reference model to address these challenges. The reference model used in this article is referred to as MRAC−EHO. MRAC−EHO employs direct model-referenced adaptive control (MRAC) with an Elephant Herding Optimization (EHO). FCWCARMA compensates for the user’s motor deficiencies during joystick operation. It employs an autoregressive moving average (ARMA) mathematical model, utilizing least square regression for parameter estimation. The system continuously monitors the wheelchair’s current speed, compares it with optimal speeds corresponding to terrain conditions stored in a reference table, and applies corrective adjustments to the joystick controller in real-time. To validate the efficacy of the proposed model, extensive simulations were conducted comparing wheelchair maneuverability with a reference model. Results of the experiments showed that FCWCARMA outperforms the MRAC−EHO by a factor of 9% in terms of the response time to reach the desired speed.